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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2006 StatPro Italia srl
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
#include <ql/methods/montecarlo/parametricexercise.hpp>
#include <ql/math/optimization/problem.hpp>
#include <ql/math/optimization/constraint.hpp>
namespace QuantLib {
namespace {
class ValueEstimate : public CostFunction {
public:
ValueEstimate(const std::vector<NodeData>& simulationData,
const ParametricExercise& exercise,
Size exerciseIndex);
Real value(const Array& parameters) const;
Disposable<Array> values(const Array&) const {
QL_FAIL("values method not implemented");
}
private:
const std::vector<NodeData>& simulationData_;
const ParametricExercise& exercise_;
Size exerciseIndex_;
mutable std::vector<Real> parameters_;
};
ValueEstimate::ValueEstimate(
const std::vector<NodeData>& simulationData,
const ParametricExercise& exercise,
Size exerciseIndex)
: simulationData_(simulationData), exercise_(exercise),
exerciseIndex_(exerciseIndex),
parameters_(exercise.numberOfParameters()[exerciseIndex]) {
for (Size i=0; i<simulationData_.size(); ++i) {
if (simulationData_[i].isValid)
return;
}
QL_FAIL("no valid paths");
}
Real ValueEstimate::value(const Array& parameters) const {
std::copy(parameters.begin(), parameters.end(),
parameters_.begin());
Real sum = 0.0;
Size n = 0;
for (Size i=0; i<simulationData_.size(); ++i) {
if (simulationData_[i].isValid) {
++n;
if (exercise_.exercise(exerciseIndex_,
parameters_,
simulationData_[i].values))
sum += simulationData_[i].exerciseValue;
else
sum += simulationData_[i].cumulatedCashFlows;
}
}
return -sum/n;
}
}
Real genericEarlyExerciseOptimization(
std::vector<std::vector<NodeData> >& simulationData,
const ParametricExercise& exercise,
std::vector<std::vector<Real> >& parameters,
const EndCriteria& endCriteria,
OptimizationMethod& method) {
Size steps = simulationData.size();
parameters.resize(steps-1);
for (Size i=steps-1; i!=0; --i) {
const std::vector<NodeData>& exerciseData = simulationData[i];
parameters[i-1].resize(exercise.numberOfParameters()[i-1]);
// optimize
ValueEstimate f(exerciseData, exercise, i-1);
Array guess(parameters[i-1].size());
exercise.guess(i-1, parameters[i-1]);
std::copy(parameters[i-1].begin(), parameters[i-1].end(),
guess.begin());
NoConstraint c;
Problem p(f, c, guess);
method.minimize(p, endCriteria);
Array result = p.currentValue();
std::copy(result.begin(), result.end(),
parameters[i-1].begin());
std::vector<NodeData>& previousData = simulationData[i-1];
for (Size j=0; j<previousData.size(); ++j) {
if (exerciseData[j].isValid) {
if (exercise.exercise(i-1,
parameters[i-1],
exerciseData[j].values))
previousData[j].cumulatedCashFlows +=
exerciseData[j].exerciseValue;
else
previousData[j].cumulatedCashFlows +=
exerciseData[j].cumulatedCashFlows;
}
}
}
Real sum = 0.0;
const std::vector<NodeData>& initialData = simulationData.front();
for (Size i=0; i<initialData.size(); ++i)
sum += initialData[i].cumulatedCashFlows;
return sum/initialData.size();
}
}
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